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The article discusses the importance of early identification of prefrailty and frailty in older adults within the community, emphasizing collaboration among healthcare professionals for proactive frailty interventions. It introduces the Prefrailty scale for Appearance Observation by Nurses (PAON) as a simple yet effective tool for identifying prefrailty through clinical observations by nurses, highlighting its potential to improve early detection and intervention strategies for frailty.

Background: Previous studies have demonstrated associations between gut microbiota, microbial metabolites, and cognitive decline. However, relationships between these factors and lipopolysaccharides (LPS; molecules of the outer membrane of gram-negative bacteria) remain controversial.Objective: To evaluate associations between plasma LPS, gut microbiota, and cognitive function.Methods: We performed a cross-sectional sub-analysis of data of 127 participants (women: 58%, mean age: 76 years) from our prospective cohort study regarding the relationship between gut microbiota and cognitive function. We enrolled patients who visited our memory clinic and assessed demographics, dementia-related risk factors, cognitive function, brain imaging, gut microbiomes, and microbial metabolites. We evaluated relationships between cognitive decline and plasma LPS using multivariable logistic regression analyses.Results: Plasma LPS concentration increased with increasing degree of cognitive decline and total cerebral small vessel disease (SVD) score (Kruskal-Wallis test; p = 0.016 and 0.007, respectively). Participants with high plasma LPS concentrations tended to have lower concentrations of gut microbial metabolites, such as lactic acid and acetic acid, and were less likely to consume fish and shellfish (44.7% versus 69.6%, p = 0.027) than those with low plasma LPS concentrations. Multivariable analyses revealed that plasma LPS concentration was independently associated with the presence of mild cognitive impairment in participants without dementia (odds ratio: 2.09, 95% confidence interval: 1.14-3.84, p = 0.007).Conclusion: In this preliminary study, plasma LPS concentration was associated with both cognitive decline and cerebral SVD and significantly correlated with beneficial gut microbial metabolites. Plasma LPS may be a risk factor for cognitive decline. [ABSTRACT FROM AUTHOR]

Background: Previous studies have demonstrated associations between gut microbiota, microbial metabolites, and cognitive decline. However, relationships between these factors and neurofilament light chain (NfL; a disease-nonspecific biomarker of neural damage) remain controversial.Objective: To evaluate the associations between plasma NfL, gut microbiota, and cognitive function.Methods: We performed a cross-sectional sub-analysis of data from our prospective cohort study that was designed to investigate the relationship between gut microbiota and cognitive function. Patients who visited our memory clinic were enrolled and demographics, dementia-related risk factors, cognitive function, brain imaging, gut microbiomes, and microbial metabolites were assessed. We evaluated the relationships between the gut microbiome, microbial metabolites, and plasma NfL. Moreover, the relationships between plasma NfL and cognitive function were assessed using multivariable logistic regression analyses.Results: We analyzed 128 participants (women: 59%, mean age: 74 years). Participants with high (above the median) plasma NfL concentrations tended to be older, women, and hypertensive and have a history of stroke, chronic kidney disease, and dementia. Plasma NfL was also associated with cerebral small vessel disease. However, plasma NfL levels were not significantly correlated with gut microbial metabolites. Multivariable analyses revealed that a higher plasma NfL concentration was independently associated with the presence of dementia (odds ratio: 9.94, 95% confidence interval: 2.75-48.2, p < 0.001).Conclusion: High plasma NfL concentration was independently associated with the presence of dementia as previously reported. However, plasma NfL levels were not significantly correlated with gut microbial metabolites in this preliminary study. [ABSTRACT FROM AUTHOR]

Aim: Community settings often need simple screening, rather than detailed tests, to identify cognitive impairment. This study aimed to develop models to screen older adults with cognitive impairment. Methods: This study used data from the Integrated Research Initiative for Living Well with Dementia Cohort Study and included 5830 older adults. Individuals were considered cognitively impaired if their Mini‐Mental State Examination score was less than 24. Three screening models were developed: the simple model (age, sex, and education), the base model comprising 13 candidate variables available in the questionnaire, and the enhanced model, where grip strength and gait speed were added to the base model. We performed binary logistic regression analysis with stepwise backward elimination (P < 0.1 for retention in the model) to develop each model. Then, we calculated integer scores from coefficients to develop score‐based models. The area under the receiver operating characteristic curve (AUC) was used to evaluate discrimination. Results: Participants with cognitive impairment accounted for 4.0% (n = 233) of the total. The score‐based simple model comprised three variables (AUC = 0.72, sensitivity: 72%, specificity: 61%). The score‐based base model included nine variables (AUC = 0.76, sensitivity: 70%, specificity: 67%). The score‐based enhanced model comprised eight variables, including grip strength and gait speed (AUC = 0.79, sensitivity: 73%, specificity: 70%). Conclusions: This study developed three screening models with acceptable discriminant validity for cognitive impairment. These models comprised simple questionnaire‐based items and common physical performance measurements. These models could enable screening of older adults suspected of cognitive impairment without the need to conduct cognitive tests in community settings. Geriatr Gerontol Int 2022; 22: 292–297. [ABSTRACT FROM AUTHOR]